A library that wraps multiple LLM providers into a consistent API while using each provider's native SDK internally, supporting multimodal I/O, file processing, and stream output.
Project description
LLM Bridge
LLM Bridge is a Python library that wraps multiple LLM providers into a consistent API while using each provider's native SDK internally, supporting multimodal I/O, file processing, and stream output.
GitHub: https://github.com/windsnow1025/LLM-Bridge
PyPI: https://pypi.org/project/LLM-Bridge/
Workflow and Features
- Message Preprocessor: extracts text content from documents (Word, Excel, PPT, Code files, PDFs) which are not natively supported by the target model.
- Chat Client Factory: creates a client for the specific LLM API with model parameters
- Model Message Converter: converts general messages to model messages
- Media Processor: converts general media (Image, Audio, Video, PDF) to model compatible formats.
- Model Message Converter: converts general messages to model messages
- Chat Client: generate stream or non-stream responses
- Model Thoughts: captures the model's thinking process
- Code Execution: generates and executes Python code
- Web Search: generates response from search results
- Token Counter: tracks and reports input and output token usage
Supported Features for API Types
The features listed represent the maximum capabilities of each API type supported by LLM Bridge.
| API Type | Input Format | Capabilities | Output Format |
|---|---|---|---|
| OpenAI Completion API | Text, Image, PDF | Thinking, Structured Output | Text |
| OpenAI Responses API | Text, Image, PDF | Thinking, Web Search, Code Execution, Structured Output | Text, Image |
| Google GenAI | Text, Image, PDF, Audio, Video | Thinking, Web Search, Code Execution, Structured Output | Text, Image, File |
| Anthropic | Text, Image, PDF | Thinking, Web Search, Code Execution, Structured Output | Text, File |
| xAI | Text, Image, PDF, Audio, Video, docx, xlsx, pptx | Thinking, Web Search, Code Execution, Structured Output | Text |
Planned Features
- More features for API Types
- Native support for Grok
Development
Python uv
- Install uv:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" - Install Python in uv:
uv python install 3.12; upgrade Python in uv:uv python upgrade 3.12 - Configure requirements:
uv sync --refresh
PyCharm
Add New Interpreter >> Add Local Interpreter
- Environment: Select existing
- Type: uv
Usage
Copy ./usage/.env.example and rename it to ./usage/.env, then fill in the environment variables.
Build
uv build
Release history Release notifications | RSS feed
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